Congratulations to Haiyi Mao for the acceptance of his first author paper to the proceedings track of the Machine Learning for Health (ML4H) 2022 Symposium. The paper is entitled “Towards Cross-Modal Causal Structure and Representation Learning”. This paper introduces a new method to perform causal discovery between structured data (e.g., clinical variables) and unstructured data (e.g., clinical images). The new method the paper describes (Cross-Modal variational Causal representation and structure Learning – CMCL) jointly learns identifiable representations given a set of independent structured variables. This paper open the way to apply causal modeling methods on complex biomedical and clinical datasets.
Congratulations to Haiyi and his co-authors!